A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33584821", "kind": "science-record", "revision": 6, "content_hash": "sha256:fd20759533e9b43956c5519dbe6db9f499095ad776f23a99e09ce38cde4f2fe2", "created_at": "2026-11-01T15:41:25Z", "updated_at": "2026-11-01T21:24:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs.", "doi": "10.3389/fgene.2020.634195", "pmid": "33584821", "authors": ["Zhijian Huang", "Chen Xiao", "Fushou Zhang", "Zhifeng Zhou", "Liang Yu", "Changsheng Ye", "Weiwei Huang", "Nani Li"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC. Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed. Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells. Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.", "claims": [{"id": "claim-method", "statement": "The prognostic immune-related lncRNAs were screened out from the TCGA data set using the univariate Cox regression method, and the statistical standard of p < 0.05 was used.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33584821", "locator": "sections[3] \"Data Download, Pre-processing and Screening of Prognostic IRGs\" paragraph 2", "quote": "The prognostic immune-related lncRNAs were screened out from the TCGA data set using the univariate Cox regression method, and the statistical standard of p < 0.05 was used."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33584821", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33584821"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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